DOCSLIB.ORG

Frequency Relationships of Air Pollution: a Case Study for PM2.5

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Frequency Relationships of Air Pollution: a Case Study for PM2.5 International Journal of Environmental Research and Public Health Article Establishment of Regional Concentration–Duration– Frequency Relationships of Air Pollution: A Case Study for PM2.5 Hone-Jay Chu * and Muhammad Zeeshan Ali Department of Geomatics, National Cheng Kung University, Tainan 70101, Taiwan; [email protected] * Correspondence: [email protected] Received: 5 February 2020; Accepted: 19 February 2020; Published: 22 February 2020 Abstract: Poor air quality usually leads to PM2.5 warnings and affects human health. The impact of frequency and duration of extreme air quality has received considerable attention. The extreme concentration of air pollution is related to its duration and annual frequency of occurrence known as concentration–duration–frequency (CDF) relationships. However, the CDF formulas are empirical equations representing the relationship between the maximum concentration as a dependent variable and other parameters of interest, i.e., duration and annual frequency of occurrence. As a basis for deducing the extreme CDF relationship of PM2.5, the function assumes that the extreme concentration is related to the duration and frequency. In addition, the spatial pattern estimation of extreme PM2.5 is identified. The regional CDF identifies the regional extreme concentration with a specified duration and return period. The spatial pattern of extreme air pollution over 8 h duration shows the hotspots of air quality in the central and southwestern areas. Central and southwestern Taiwan is at high risk of exposure to air pollution. Use of the regional CDF analysis is highly recommended for efficient design of air quality management and control. Keywords: PM2.5; duration; frequency; spatial map 1. Introduction Air pollution has become a worldwide environmental health risk [1,2], especially in the developing countries. In management, an essential task is to understand the duration, frequency, and intensity of air pollution [3,4]. Poor air quality usually affects human health [5–7]. Elderly people, children, and people with heart or lung conditions are more sensitive than others to the effects of inhaling fine particles (PM2.5). Acute exposure to PM2.5 is associated with various health outcomes, such as respiratory symptoms [8,9], hospital admissions, or death [10]. The maximum PM2.5 concentrations have significant positive impacts on outpatient visits [11]. Chronic exposure to PM2.5 can lead to chronic diseases or reduced life expectancy [12]. The risk that extreme pollutant concentrations pose to human health is the subject of environmental concern [10]. The impact of frequency and duration of air quality has received considerable attention over the past decades. However, few people discuss the concentration–duration–frequency (CDF) relationships in air pollution. The CDF is modified from the intensity–duration–frequency (IDF) relationship, which is a mathematical relationship between the rainfall intensity, duration, and annual frequency of occurrence [13]. The most common IDF techniques from hydrological engineering were used for Gumbel distribution [14]. However, the CDF is related to the extreme concentration of air pollution with its duration and annual frequency of occurrence. The CDF formulas are empirical equations representing a relationship between the maximum concentration as a dependent variable and other parameters of interest, i.e., duration and annual frequency of occurrence. In the frequency aspect, the Int. J. Environ. Res. Public Health 2020, 17, 1419; doi:10.3390/ijerph17041419 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020, 17, 1419 2 of 13 return period of an event might be 100 years and is expressed as its probability of occurring being 1/100, or 1% in any one year. Many types of distributions, such as log-normal, Weibull, Pearson, gamma, and Gumbel distributions, are used to fit the distribution of air pollutant concentration [10,15–18]. The probability of occurrence and the CDF of extreme PM2.5 can be investigated using standard statistical techniques, e.g., Gumbel, log-normal, and Weibull probability distributions. In this study, the CDF analysis of fine aerosols was performed using the Gumbel probability distribution. The analysis system uses the actual measurement records to calculate the occurrence values of different frequencies according to the statistical properties of the samples and presumes the possible devaluation values to derive the basis of the extreme CDF relationship. Moreover, the empirical CDF relationship in this study is assumed to be the Bernard formula function [13,19]. The coefficient estimation is performed in a regression, and the estimated extreme concentration based on the defined return period and duration is finally obtained. The CDF analysis for a single station can be derived from historical data of PM2.5. However, the spatial pattern of air pollution is heterogeneous [20]. The regional CDF function is developed and determined on the basis of the regional parameters. Based on interpolation, the regional parameters can be estimated. The regional CDF analysis provides spatial patterns of extreme air pollution and further provides detailed spatial patterns in areas where no observations are available. The model estimates the extreme concentrations at unknown locations by simultaneously considering the regional parameters and using the regional CDF function as proxy information to provide information on the relationships of spatial patterns of air quality with various durations and return periods. Using the regional CDF analysis, the hotspot area of air pollution with a specified duration and return period can be identified. The objective of the study was to develop the relationship between the extreme PM2.5 concentration, the duration, and the frequency of occurrence. As a basis for deducing the Gumbel extreme value distribution and the empirical CDF model, the extreme concentration was assumed to be related to the duration and frequency, and the coefficient estimation was carried out in a regression. Furthermore, regional CDF estimations were conducted. It was expected that the regional CDF would identify the regional extreme concentration map with a specified duration and return period. 2. Materials and Study Area Figure1 shows the locations of air quality monitoring stations in Taiwan. Air quality zones, e.g., northern, central, southwestern, and eastern, are also established in Taiwan (Figure1). The Taiwanese Environmental Protection Agency (TWEPA) has been regularly recording the air quality and meteorological data throughout Taiwan since July 1982 [20]. Since August 2005, the TWEPA has completed the installation of 76 monitoring instruments for fine airborne aerosols (PM2.5; Figure1). In this study, the historical data obtained by the TWEPA’s Air Monitoring Network were obtained for the PM2.5 hourly fine aerosol concentration in Taiwan from 2005 to 2015. This study used the hourly PM2.5 obtained by the TWEPA stations that are in compliance with the regulatory air monitoring procedures. Int. J. Environ. Res. Public Health 2020, 17, 1419 3 of 13 Int. J. Environ. Res. Public Health 2020, 17, x FOR PEER REVIEW 3 of 13 FigureFigure 1.1. LocationsLocations ofof PMPM2.52.5 monitoringmonitoring stations stations.. 3.3. Method Methodss First,First, the moving average ofof PM2.52.5 datadata for for various various durations durations need neededed to be prepared. Derivations of the PM2.5 CCDFDF relationships relationships for for each each station station for for the different different return periodsperiods were determined by fittingfitting the Gumbel distribution to the correspondingcorresponding maximum concentration per year at various stations (Section(Section 3.13.1)).. The empirical model for thethe PMPM2.52.5 CDFCDF analysis at each station was developed (Section(Section 3.23.2)).. Eventually, thethe regionalregional PMPM2.52.5 CDF analysis w wasas performed ( (SectionSection 3.3)3.3).. 3.1. Derivations of PM2.5 CDF 3.1. Derivations of PM2.5 CDF The probability of occurrence of the extreme pollution event (C c) for the annual frequency of The probability of occurrence of the extreme pollution event (퐶≥≥ 푐) for the annual frequency F exceedanceof exceedance ( ) ( is퐹) the is the inverse inverse of theof the return return period period calculated calculated as follows:as follows: 11 P푃((C퐶 ≥c푐))==F퐹== (1(1)) ≥ T푇 The probability ofof non-exceedancenon-exceedance isis asas follows:follows: 1 푃(퐶 ≤ 푐) = 1 −1 (2) P(C c) = 1 푇 (2) ≤ − T where 푇 is the return period or recurrence interval of extreme concentration, 퐶 is the extreme PM2.5 whereconcentrationT is the, returnand 푐 is period the threshold or recurrence. interval of extreme concentration, C is the extreme PM2.5 concentration,The Gumbel distribution and c is the was threshold. fitted to the extreme PM2.5 data, i.e., to the maximum annual values. The cumulative Gumbel extreme value distribution is illustrated as follows [21]: −α(퐶−β) 푃(퐶 ≤ 푐) = 푒−푒 (3) Int. J. Environ. Res. Public Health 2020, 17, 1419 4 of 13 The Gumbel distribution was fitted to the extreme PM2.5 data, i.e., to the maximum annual values. The cumulative Gumbel extreme value distribution is illustrated as follows [21]: e α(C β) P(C c) = e− − − (3) ≤ where P(C c) is the probability of non-exceedance, and α and β are the parameters of the Gumbel ≤ distribution. From the observed sequence, the parameters can be estimated [13]. The parameters α and β are linked to the mean value (C) of the extreme PM2.5 data and to the standard deviation (σC) of the extreme PM2.5 data through the following equations: 1.282 α = (4) σC 0.577σ β = C C = C 0.45σ (5) − 1.282 − C The logarithm was taken twice to yield a formulation from Equation (3), and Equation (2) was merged into Equation (3). Thus, the extreme concentration of PM2.5 (C) for each return period is as follows: 1 C = β ln(lnT ln(T 1)) (6) − α − − In addition, C can be expressed as a function of the return period for each duration. The average extreme concentrations for various durations were calculated based on the aforementioned steps.
Load more

Recommended publications
  • COMEAP: Review of the UK Air Quality Index
    Review of the UK Air Quality Index A report by the Committee on the Medical Effects of Air Pollutants Chairman: Professor J G Ayres Chairman of the Standards Advisory Subgroup: Professor S Holgate © Crown Copyright 2011 Produced by the Health Protection Agency for the Committee on the Medical Effects of Air Pollutants ISBN 978-0-85951-699-0 Foreword This is the first report prepared by the Standards Advisory Subgroup (CSAS) of the Committee on the Medical Effects of Air Pollutants. The subgroup was formed in June 2009 when the Expert Panel on Air Quality Standards (EPAQS) was merged with COMEAP. The work of the subgroup is supported by a joint Secretariat from both the Department for Environment, Food and Rural Affairs (Defra) and the Health Protection Agency. In 2009, the subgroup was asked by Defra to review the current UK air quality index. Since the air quality index was last revised in 1998, there have been a number of developments in the regulation of air pollution and in the presentation of air quality information, which are reflected in these proposals. COMEAP is very keen to enhance the way we communicate facts about air pollution to the public and the findings from the public insight study, which was commissioned by Defra, have been invaluable for developing the health advice and the presentation of the index. I am grateful to Stephen Holgate, the Chairman of the Standards Advisory Subgroup, who has ensured that the needs of the public for clear information were central to the development of these proposals. I am also grateful to the Secretariat and Members of the subgroup who developed the report and, indeed, to all Members of COMEAP.
    [Show full text]
  • Author's Tracked Changes
    Carbonaceous aerosol composition in air masses influenced by large- scale biomass burning: a case-study in Northwestern Vietnam Dac-Loc Nguyen1,2,8, Hendryk Czech1,2, Simone M. Pieber3, Jürgen Schnelle-Kreis1, Martin Steinbacher3, Jürgen Orasche1, Stephan Henne3, Olga B. Popovicheva4, Gülcin Abbaszade1, Gueünter Engling5,a, 5 Nicolas Bukowiecki6,b, Nhat-Anh Nguyen7, Xuan-Anh Nguyen8, Ralf Zimmermann1,2 1Joint Mass Spectrometry Centre (JMSC), Cooperation Group “Comprehensive Molecular Analytics” (CMA), Helmholtz Zentrum München, München, 81379, Germany 2Joint Mass Spectrometry Centre (JMSC), Chair of Analytical Chemistry, University of Rostock, Rostock, 18059, Germany 3Empa, Laboratory for Air Pollution/Environmental Technology, Dübendorf, 8600, Switzerland 10 4Skobeltsyn Institute of Nuclear Physics, Moscow State University, Moscow, 119991, Russian Federation 5Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 30013, Taiwan 6Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen, 5232, SwitzerlandDepartment of Environmental Sciences, University of Basel, Basel, 4056, Switzerland 7Hydro-Meteorological Observation Center, Vietnam Meteorological and Hydrological Administration, Ministry of Natural 15 Resources and Environment, Ha Noi, Vietnam 8Institute of Geophysics, Vietnam Academy of Science and Technology (VAST), Ha Noi, Vietnam anow at: Mobile Source Laboratory Division, California Air Resource Board, El Monte, CA 91731, United States bnow at: Department of Environmental Sciences, University of Basel, Basel, 4056, Switzerland Correspondence to: Hendryk Czech ([email protected]) 20 Abstract. We investigated concentrations of organic carbon (OC), elemental carbon (EC) and a wide range of particle-bound organic compounds in daily sampled PM2.5 at the remote Pha Din (PDI) - Global Atmosphere Watch (GAW) monitoring station in Northwestern Vietnam during an intense 3-week sampling campaign from 23rd March to 12th April 2015.
    [Show full text]
  • Regulatory Impact Analyses for the Particulate Matter and Ozone
    REGULATORY IMPACT ANALYSES FOR THE PARTICULATE MATTER AND OZONE NATIONAL AMBIENT AIR QUALITY STANDARDS AND PROPOSED REGIONAL HAZE RULE Prepared by Innovative Strategies and Economics Group Office of Air Quality Planning and Standards U.S. Environmental Protection Agency Research Triangle Park, N.C. July 17, 1997 TABLE OF CONTENTS TABLE OF CONTENTS.............................................................................................................. i SELECTED LIST OF ACRONYMS.............................................................................................vii EXECUTIVE SUMMARY........................................................................................................ES-1 1.0 INTRODUCTION AND OVERVIEW................................................................................1-1 1.1 THE NATIONAL AIR QUALITY CHALLENGE.............................................................1-2 1.2 OVERVIEW OF THE RIA METHODOLOGY..................................................................1-6 1.3 KEY IMPROVEMENTS OVER THE PROPOSAL RIA’S..............................................1-10 1.4 KEY LIMITATIONS.........................................................................................................1-12 1.5 REFERENCES...................................................................................................................1-14 2.0 STATEMENT OF NEED FOR THE PROPOSED REGULATIONS................................2-1 2.1 INTRODUCTION...............................................................................................................2-1
    [Show full text]
  • Investigating a Potential Map of PM2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan
    International Journal of Environmental Research and Public Health Article Investigating a Potential Map of PM2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan Yuan-Chien Lin 1,2,* , Hua-San Shih 1, Chun-Yeh Lai 1 and Jen-Kuo Tai 1,3 1 Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan; [email protected] (H.-S.S.); [email protected] (C.-Y.L.); [email protected] (J.-K.T.) 2 Research Center for Hazard Mitigation and Prevention, National Central University, Taoyuan 32001, Taiwan 3 Fire Bureau, Hsinchu County Government, Hsinchu County 30295, Taiwan * Correspondence: [email protected] Received: 28 October 2020; Accepted: 19 November 2020; Published: 23 November 2020 Abstract: In the past few years, human health risks caused by fine particulate matters (PM2.5) and other air pollutants have gradually received attention. According to the Disaster Prevention and Protection Act of Taiwan’s Government enforced in 2017, “suspended particulate matter” has officially been acknowledged as a disaster-causing hazard. The long-term exposure to high concentrations of air pollutants negatively affects the health of citizens. Therefore, the precise determination of the spatial long-term distribution of hazardous high-level air pollutants can help protect the health and safety of residents. The analysis of spatial information of disaster potentials is an important measure for assessing the risks of possible hazards. However, the spatial disaster-potential characteristics of air pollution have not been comprehensively studied. In addition, the development of air pollution potential maps of various regions would provide valuable information. In this study, Hsinchu County was chosen as an example.
    [Show full text]
  • Outdoor Air Pollution and Health in the Developing Special Report 18 November 2010 Countries of Asia: a Comprehensive Review
    SPECIAL REPO SPECIAL R E P O R T 18 R T 18 HE A L TH HE A L TH Outdoor Air Pollution and Health EFF E CTS EFF E CTS INSTITUTE Countries of Asia: Review A Comprehensive Outdoor Air and in Pollution Health the Developing INSTITUTE in the Developing Countries of Asia: 101 Federal Street, Suite 500 November 2010 A Comprehensive Review Boston, MA 02110, USA +1-617-488-2300 www.healtheffects.org HEI International Scientific Oversight Committee SPECIAL REPO rT 1 8 November 2010 November 2010 Outdoor Air Pollution and Health in the Developing Countries of Asia: A Comprehensive Review HEI International Scientific Oversight Committee Special Report 18 Health Effects Institute Boston, Massachusetts Trusted Science · Cleaner Air · Better Health Publishing history: The Web version of this document was posted at www.healtheffects.org in November 2010. Citation for document: HEI International Scientific Oversight Committee. 2010. Outdoor Air Pollution and Health in the Developing Countries of Asia: A Comprehensive Review. Special Report 18. Health Effects Institute, Boston, MA. When specifying a section of this report, cite it as a chapter of the whole document. © 2010 Health Effects Institute, Boston, Mass., U.S.A. Cameographics, Belfast, Me., Compositor. Printed by Recycled Paper Printing, Boston, Mass. Library of Congress Number for the HEI Report Series: WA 754 R432. Cover paper: made with at least 55% recycled content, of which at least 30% is post-consumer waste; free of acid and elemental chlorine. Text paper: made with 100% post-consumer waste recycled content; acid free; no chlorine used in processing. The book is printed with soy-based inks and is of permanent archival quality.
    [Show full text]
  • Climate Change Or Air Quality Improvement?
    Aerosol and Air Quality Research, 19: 896–910, 2019 Copyright © Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2018.04.0152 Trends of Fog and Visibility in Taiwan: Climate Change or Air Quality Improvement? Maylin Maurer1*, Otto Klemm1**, Hanna L. Lokys1, Neng-Huei Lin2 1 Climatology Working Group, University of Münster, 48149 Münster, Germany 2 Department of Atmospheric Sciences, National Central University, Taoyuan 32001, Taiwan ABSTRACT This study provides insight into how the visibility in Taiwan has varied over time and what the main drivers of these visibility changes are. From 1985 to 2016, urban inland sites showed increases in visibility and decreases in the frequency of hazy and/or foggy days, whereas urbanized and rural coastal regions either showed no clear trend or even an overall decrease in visibility. Over the most recent 5 to 10 years, a consistent increase in the visibility and decrease in the haze frequency has been found for most of the stations, except for the rural to suburban regions. In general, visibility is driven by the relative humidity (rH) and the mass concentration of aerosol particles (PM). On the one hand, the combination of climate change and urbanization, resulting in a rise in temperature (on average, +0.035°C y–1) and an associated overall decrease in rH (on average, –0.125% y–1), has had a positive influence on long-term visibility in the cities of Taiwan. On the other hand, improvements in air quality supported the increase in visibility during the late 2000s and early 2010s.
    [Show full text]
  • Dissertation Quantifying the Economic
    DISSERTATION QUANTIFYING THE ECONOMIC HEALTH COST OF EXPOSURE TO WILDFIRE SMOKE: FOUR ESSAYS IN NON-MARKET VALUATION, METHODOLOGICAL COMAPARISONS, AND ECONOMETRIC METHODS TO ADDRESS ENDOGENEITY Submitted by Leslie A. Richardson Department of Agricultural and Resource Economics In partial fulfillment of the requirements For the Degree of Doctor of Philosophy Colorado State University Fort Collins, Colorado Spring 2011 Doctoral Committee: Advisor: John Loomis Patricia Champ Andy Seidl Robert Kling ABSTRACT QUANTIFYING THE ECONOMIC HEALTH COST OF EXPOSURE TO WILDFIRE SMOKE: FOUR ESSAYS IN NON-MARKET VALUATION, METHODOLOGICAL COMAPARISONS, AND ECONOMETRIC METHODS TO ADDRESS ENDOGENEITY Wildfires and their proximity to urban areas have become more frequent, yet few economic studies have looked closely at the welfare implications exposure to wildfire smoke has on affected individuals. Further, there is a growing concern that human health impacts resulting from this exposure are ignored in estimates of the monetized damages from a given wildfire. Current research highlights the need for better data collection and analysis of these impacts. Using unique primary data, this dissertation quantifies the economic health cost of exposure to wildfire smoke using non-market valuation techniques including the contingent valuation and defensive behavior methods. The individual willingness to pay for a reduction in symptom days as well as perceived pollution levels are quantified and compared to a simple cost of illness estimate. Results indicate that many residents surveyed did not seek medical attention for major health effects, but rather suffered from minor health impacts whose cost is not captured in a cost of illness estimate. As a result, ii expenditures on defensive activities and the disutility associated with symptoms and lost leisure are found to be substantial for the case of wildfire smoke exposure.
    [Show full text]
  • Chrys Yang 6/11/2021 Research Paper - Air Pollution in Taiwan
    Chrys Yang 6/11/2021 Research Paper - Air pollution in Taiwan In Taichung, the sky always seems gloomy and greyish. The sky is regularly covered with clouds, like it is about to rain. But those aren’t clouds. For children living in Taichung, seeing a blue sky might be their dream since it’s rare to see blue sky in that city. The sky looks so gloomy that there are barely any stars visible at night. This is the situation in other cities in Taiwan as well, and it is becoming more common. Smoggy skies are one of the most visible effects of air pollution on the environment. Air pollution harms people’s health and causes hazardous diseases. Exposure to pollutants will not only damage people’s lungs, it will also hurt the whole circulatory system. Too much exposure to pollutants leads to lung cancer, and even diabetes. Moreover, air pollution is the main factor driving global warming. In 1975, the Executive Yuan of Taiwan formulated the Air Pollution Control Act to control pollutants such as PM10 and carbon monoxide. This was when the government started to notice and deal with this issue. In 2010, there was a protest against the Kuokuang Petrochemical Technology Company project, since this factory was going to emit a large amount of PM2.5. The government then opposed the project. After this incident, both the citizens and the government of Taiwan started to pay more attention to air pollution problems. It has been eleven years since the incident, but air pollution is still a major problem.
    [Show full text]
  • Synoptic Weather Patterns and Associated Air Pollution in Taiwan
    Aerosol and Air Quality Research, 19: 1139–1151, 2019 Copyright © Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2018.09.0348 Synoptic Weather Patterns and Associated Air Pollution in Taiwan Chia-Hua Hsu, Fang-Yi Cheng* Department of Atmospheric Sciences, National Central University, Taoyuan 32001, Taiwan ABSTRACT In this study, cluster analysis is applied to the daily averaged wind fields and sea-level pressure observed at surface weather stations in Taiwan from January 2013 to March 2018 to classify the synoptic weather patterns and study the characteristics of corresponding air pollutants, including fine particulate matter (PM2.5), coarse particulate matter (PM10), and ozone (O3). The classification identified six weather types: Clusters 1, 2, and 3 (C1–C3), which are typical winter weather types and associated with high air pollutant concentrations—C3, in particular, influenced by weak synoptic weather, is associated with the lowest wind speeds and the highest PM2.5 and PM10 concentrations and represents the most prevalent weather type that is prone to the occurrence of PM2.5 events; C4, which occurs mostly during seasonal transition months and is associated with the highest O3 concentrations; and C5 and C6, which are summer weather types with low air pollutant concentrations. Further analysis of the local wind flow using the 0.3° ERA5 reanalysis dataset and surface-observed wind data indicates that in western Taiwan, the land-sea breeze is embedded within the synoptic weather type of C3, which is favorable to air pollutant accumulation. However, when the prevailing northeasterly wind is obstructed by the Central Mountain Range, southwestern Taiwan, being situated on the leeside of the mountains, often exhibits the worst air pollution due to stagnant wind conditions.
    [Show full text]
  • Health Risk Assessment for Residents Exposed to Atmospheric Diesel Exhaust Particles in Southern Region of Taiwan
    Atmospheric Environment 85 (2014) 64e72 Contents lists available at ScienceDirect Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv Health risk assessment for residents exposed to atmospheric diesel exhaust particles in southern region of Taiwan Chia-Pin Chio a, Chung-Min Liao b,*, Ying-I Tsai c, Man-Ting Cheng d, Wei-Chun Chou e a Institute of Occupational Medicine and Industrial Hygiene, College of Public Health, National Taiwan University, Taipei 100, Taiwan, ROC b Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 106, Taiwan, ROC c Department of Environmental Resources Management, Chia Nan University of Pharmacy and Science Tainan 717, Taiwan, ROC d Department of Environmental Engineering, National Chung Hsing University, Taichung 402, Taiwan, ROC e Department of Biomedical Engineering and Environmental Science, National Tsing Hua University, Hsinchu 300, Taiwan, ROC highlights DEP dose and cancer risk estimates showed heterogeneously spatiotemporal difference. DNA damage biomarker and cancer incidence estimates had a positive association. Resident health risk from atmospheric DEP depending on measured data type. Health risk assessments of air pollution can guide adaptive mitigation strategies. article info abstract Article history: Evidence shows a strong association among air pollution, oxidative stress (OS), deoxyribonucleic acid Received 13 September 2013 (DNA) damage, and diseases. Recent studies indicated that the aging, human neurodegenerative diseases Received in revised form and cancers resulted from mitochondrial dysfunction and OS. The purpose of this study is to provide a 27 November 2013 probabilistic risk assessment model to quantify the atmospheric diesel exhaust particles (DEP)-induced Accepted 28 November 2013 pre-cancer biomarker response and cancer incidence risk for residents in south Taiwan.
    [Show full text]
  • An Overview of Regional Experiments on Biomass Burning Aerosols and Related Pollutants in Southeast Asia: from BASE-ASIA and the Dongsha Experiment to 7-Seasq
    Atmospheric Environment 78 (2013) 1e19 Contents lists available at SciVerse ScienceDirect Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv An overview of regional experiments on biomass burning aerosols and related pollutants in Southeast Asia: From BASE-ASIA and the Dongsha Experiment to 7-SEASq Neng-Huei Lin a,*, Si-Chee Tsay b, Hal B. Maring c, Ming-Cheng Yen a, Guey-Rong Sheu a, Sheng-Hsiang Wang a,d, Kai Hsien Chi e, Ming-Tung Chuang f, Chang-Feng Ou-Yang g, Joshua S. Fu h,i, Jeffrey S. Reid j, Chung-Te Lee f, Lin-Chi Wang k,l, Jia-Lin Wang g, Christina N. Hsu b, Andrew M. Sayer b,m, Brent N. Holben b, Yu-Chi Chu n, Xuan Anh Nguyen o, Khajornsak Sopajaree p, Shui-Jen Chen q, Man-Ting Cheng r, Ben-Jei Tsuang r, Chuen-Jinn Tsai s, Chi-Ming Peng t, Russell C. Schnell u, Tom Conway u, Chang-Tang Chang v, Kuen-Song Lin w, Ying I. Tsai x,y, Wen-Jhy Lee z, Shuenn-Chin Chang n, Jyh-Jian Liu n, Wei-Li Chiang n, Shih-Jen Huang aa, Tang-Huang Lin bb, Gin-Rong Liu bb a Department of Atmospheric Sciences, National Central University, Chung-Li, Taiwan b NASA Goddard Space Flight Center, Greenbelt, MD, USA c NASA Headquarters, Washington, DC, USA d NASA Postdoctoral Program Fellow, Goddard Space Flight Center, NASA, Greenbelt, MD, USA e Institute of Environmental and Occupational Health Sciences, National Yang Ming University, Taipei, Taiwan f Graduate Institute of Environmental Engineering, National Central University, Chung-Li, Taiwan g Department of Chemistry, National Central University, Chung-Li, Taiwan h Department
    [Show full text]
  • Using Costs and Health Benefits to Estimate the Priority of Air
    applied sciences Article Using Costs and Health Benefits to Estimate the Priority of Air Pollution Control Action Plan: A Case Study in Taiwan Hsin-Chih Lai 1, Min-Chuan Hsiao 2,*, Je-Liang Liou 3 , Li-Wei Lai 4, Pei-Chih Wu 1 and Joshua S. Fu 5 1 Department of Green Energy and Environmental Resources, Chang Jung Christian University, Tainan 71101, Taiwan; [email protected] (H.-C.L.); [email protected] (P.-C.W.) 2 Institute of Environmental Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan 3 The Center for Green Economy, Chung-Hua Institution for Economic Research, Taipei 10672, Taiwan; [email protected] 4 Environmental Research and Information Center, Chang Jung Christian University, Tainan 71101, Taiwan; [email protected] 5 Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996, USA; [email protected] * Correspondence: [email protected] or [email protected] Received: 27 July 2020; Accepted: 27 August 2020; Published: 28 August 2020 Abstract: A comparative analysis was conducted between the costs and health benefits of the Air Pollution Control Action Plan (APCAP), which can be implemented in any country to improve air quality and human health. In this study, air quality modeling was used to simulate several scenarios and implement the Kriging method to describe the PM2.5 reduction concentration instantly. Then, health benefits were estimated using the environmental benefit mapping and analysis program (BenMAP) with results from the air quality modeling and Kriging method. To estimate the priority of APCAP, 14 pollution control measures that cover point, mobile, and area sources of air pollution in Taiwan were analyzed.
    [Show full text]